Method for evaluating movement state of heart

ABSTRACT

A method for evaluating a movement state of a heart is provided and includes: obtaining a plurality of consecutive heart ultrasound images corresponding to a heart and accordingly estimating a plurality of left ventricular volumes corresponding to the heart ultrasound images; finding a plurality of specific extremums in the left ventricular volumes and accordingly estimating a plurality of time differences among the specific extremums; estimating a statistical characteristic value of the time differences based on the time differences; and determining that an abnormal movement state of the heart occurs in response to determining that at least one of the time differences deviates from the statistical characteristic value up to a predetermined threshold.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the priority benefit of Taiwan applicationserial no. 110109633, filed on Mar. 17, 2021. The entirety of theabove-mentioned patent application is hereby incorporated by referenceherein and made a part of this specification.

BACKGROUND Technical Field

The disclosure relates to a method for evaluating a medical image, andin particular, to a method for evaluating a movement state of a heart.

Description of Related Art

At present, the most widely used and most economical method forexamining the structure of the heart is the heart ultrasound method.When the heart of a patient is required to be examined throughultrasound, the ultrasound technician or doctor generally performs heartscanning by holding an ultrasound probe at a specific angle to checkwhether the patient's heart structure is abnormal.

In clinical practice, various ways for assessing whether the heart is ingood condition are available, and one of these is to measure the leftventricular ejection fraction (LVEF). LEVE measures how much blood isejected from the left ventricle in each heartbeat. In hospitals, manualinspection or semi-automatic methods are still used to identify the enddiastole and end systole (ES) of the heart in the heart rate cycle.After identifying the ED and ES, the built-in tool program of theultrasound machine can calculate the end-diastolic volume (EDV) and theend-systolic volume (ESV) and then calculates the ejection fraction(EF).

In the current technical field, the AI deep learning model has graduallybeen used to identify the end diastole and the end systole of the heartin the heart rate cycle, and the tedious work of manually identifyingwhether the image is ED or ES is thereby lowered.

Nevertheless, in actual clinical situations, the AI deep learning modelhas not yet replaced manual identification for two reasons. Firstly,when the patient has a heart disease, such as arrhythmia caused byatrial fibrillation, the AI deep learning model will not be able toeffectively identify ED and ES, resulting in incorrect calculation ofEF. Secondly, when the ultrasound image of the heart obtained by theultrasound probe is not clear enough, the AI deep learning model willnot be able to judge well, and it will not be able to effectivelyidentify ED and ES, resulting in incorrect calculation of EF.

Therefore, for a person having ordinary skill in the art, a mechanismthat can be designed to determine whether an abnormal movement state ofthe heart (such as arrhythmia) of a patient occurs may facilitateeffective identification of the end diastole and the end systole of theheart in the heart rate cycle performed by the above-mentioned AI deeplearning model, and a correct EF may thus be obtained.

SUMMARY

Accordingly, the disclosure provides a method for evaluating a movementstate of a heart, which may be used to solve the foregoing technicalproblems.

The disclosure provides a method for evaluating a movement state of aheart suitable for an electronic apparatus, and the method includes thefollowing steps. A plurality of consecutive heart ultrasound imagescorresponding to a heart is obtained, and a plurality of leftventricular volumes corresponding to the heart ultrasound images areaccordingly estimated. A plurality of specific extremums in the leftventricular volumes are found, and a plurality of time differences amongthe specific extremums are accordingly estimated. A statisticalcharacteristic value of the time differences is estimated based on thetime differences. An abnormal movement state of the heart is determinedto occur in response to determining that at least one of the timedifferences deviates from the statistical characteristic value up to apredetermined threshold.

To make the aforementioned more comprehensible, several embodimentsaccompanied with drawings are described in detail as follows.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are included to provide a furtherunderstanding of the disclosure, and are incorporated in and constitutea part of this specification. The drawings illustrate exemplaryembodiments of the disclosure and, together with the description, serveto explain the principles of the disclosure.

FIG. 1 is a schematic view illustrating an electronic apparatusaccording to an embodiment of the disclosure.

FIG. 2 is a flow chart illustrating a method for estimating aventricular volume according to an embodiment of the disclosure.

FIG. 3 is a view illustrating an application scenario according to anembodiment of the disclosure.

FIG. 4 is a schematic view illustrating finding of a first, second, andthird reference point pixels based on distances among the referencepoint pixels according to an embodiment of the disclosure.

FIG. 5A is a schematic view illustrating finding of a reference pointpixel corresponding to an apex according to FIG. 3.

FIG. 5B is a schematic view illustrating finding of a reference pointpixel corresponding to a left flap of a mitral valve according to FIG.5A.

FIG. 5C is a schematic view illustrating finding of a reference pointpixel corresponding to a right flap of the mitral valve according toFIG. 5B.

FIG. 6 is a flow chart illustrating a method for evaluating a movementstate of a heart according to an embodiment of the disclosure.

FIG. 7A is a view illustrating an application scenario according to anembodiment of the disclosure.

FIG. 7B is a view illustrating another application scenario according toFIG. 7A.

DESCRIPTION OF THE EMBODIMENTS

With reference to FIG. 1, which is a schematic view illustrating anelectronic apparatus according to an embodiment of the disclosure. Indifferent embodiments, an electronic apparatus 100 is, but not limitedto, a computer apparatus of any type, a smart apparatus, and/or ahandheld apparatus, for example.

As shown in FIG. 1, the electronic apparatus 100 includes a storagecircuit 102 and a processor 104. The storage circuit 102 is a fixed or amovable random access memory (RAM) in any form, a read-only memory(ROM), a flash memory, a hard disc, other similar devices, or acombination of the foregoing devices, for example, and may be used torecord a plurality of program codes or modules.

The processor 104 is coupled to the storage circuit 102 and may be aprocessor for general use, a processor for special use, a traditionalprocessor, a digital signal processor, a plurality of microprocessors,one or a plurality of microprocessors combined with a digital signalprocessor core, a controller, a microcontroller, an application specificintegrated circuit (ASIC), a field programmable gate array (FPGA)circuit, an integrated circuit of any other types, a state machine, aprocessor based on an advanced RISC machine (ARM), and the like.

In the embodiments of the disclosure, the processor 104 may access themodules and program codes in the storage circuit 102 to implement amethod for estimating a ventricular volume provided by the disclosure,and detailed description is provided as follows.

With reference to FIG. 2 and FIG. 3, FIG. 2 is a flow chart illustratinga method for estimating a ventricular volume according to an embodimentof the disclosure, and FIG. 3 is a view illustrating an applicationscenario according to an embodiment of the disclosure. The methodprovided by this embodiment may be implemented by the electronicapparatus 100 of FIG. 1, and details of the steps in FIG. 2 aredescribed as follow in accompanying with the devices shown in FIG. 1 andthe scenario of FIG. 3.

First, in step S210, the processor 104 may obtain a left ventricularmask image 31 corresponding to a heart ultrasound image 30, and the leftventricular mask image 31 is a binary image. In the embodiments of thedisclosure, the processor 104 may, for example, input an apical view ofthe heart ultrasound image 30 (e.g., an apical two chamber (A2C) view oran apical four chamber (4C) view) into a pre-trained machine learningmodel, and the corresponding binary image outputted by this machinelearning model corresponding to the heart ultrasound image 30 acts asthe left ventricular mask image 31.

In an embodiment, in order to enable the machine learning model to beequipped with the above capabilities, a designer may input various heartultrasound images of an image region marked with the ventricle astraining data into the machine learning model during training of themachine learning model. In this way, the machine learning model maylearn characteristics of the image region corresponding to the ventricleand may thus further identify the image region corresponding to theventricle when an unknown heart ultrasound image is obtained. Afterthat, the machine learning model may, but not limited to, set all pixelsin the image region corresponding to the ventricle to a first value(e.g., 255) and set all pixels in the image region not corresponding tothe ventricle to a second value (e.g., 0) to generate a correspondingbinary image.

After that, in step S220, the processor 104 may find 3 reference pointpixels 311 to 313 in the left ventricular mask image 31.

In the embodiments of the disclosure, each of the reference point pixels311 to 313 may have the first value (e.g., 255). Besides, each of thereference point pixels 311 to 313 may be surrounded by N (e.g., 8)surrounding pixels, and the surrounding pixels of each of the referencepoint pixels 311 to 313 may include N1 (e.g., 3) first surroundingpixels with the first value and N2 (e.g., 0) second surrounding pixelswith the second value (e.g., 5), where N, N1, and N2 are positiveintegers.

In an embodiment, among the surrounding pixels of the reference pointpixel 311, the first surrounding pixels (i.e., the pixels located in arange 311 a) are arranged in a straight line, and the second surroundingpixels (i.e., the pixels located in a range 311 b) are arranged in a Cshape.

In an embodiment, among the surrounding pixels of the reference pointpixel 312, the first surrounding pixels (i.e., the pixels located in arange 312 a) are arranged in an L shape, and the second surroundingpixels (i.e., the pixels located in a range 312 b) are arranged in an Lshape.

In an embodiment, among the surrounding pixels of the reference pointpixel 313, the first surrounding pixels (i.e., the pixels located in arange 313 a) are arranged in an L shape, and the second surroundingpixels (i.e., the pixels located in a range 313 b) are arranged in an Lshape.

In the embodiments of the disclosure, the reference point pixels 311 to313 are all unique in the left ventricular mask image 31, so theprocessor 104 may view each pixel in the left ventricular mask image 31one by one and define 3 pixels that meet the above conditions (e.g., thepixel having 8 surrounding pixels including 3 first surrounding pixelswith the first value and 5 second surrounding pixels with the secondvalue and having the first value) as the reference point pixels 311 to313.

Next, in step S230, the processor 104 may estimate a left ventricularvolume corresponding to the heart ultrasound image 30 based on thereference point pixels 311 to 313. In an embodiment, the processor 104may estimate distances among the reference point pixels 311 to 313 andaccordingly find a first reference point pixel, a second reference pointpixel, and a third reference point pixel corresponding to an apex, afirst flap of a mitral valve (e.g., left flap of the mitral valve), anda second flap of the mitral valve (e.g., right flap of the mitral valve)respectively among the reference point pixels 311 to 313. Next, theprocessor 104 may apply a Simpson's rule to estimate the leftventricular volume corresponding to the heart ultrasound image 30 basedon the first reference point pixel, the second reference point pixel,and the third reference point pixel.

With reference to FIG. 4, which is a schematic view illustrating findingof a first, second, and third reference point pixels based on distancesamong the reference point pixels according to an embodiment of thedisclosure. Generally, a distance between the left flap and the rightflap of the mitral valve is less than a distance between the apex andany one of the two flaps of the mitral valve. Therefore, based on thisprinciple, the processor 104 may find the first, second, and thirdreference point pixels corresponding to the apex, the first flap of themitral valve, and the second flap of the mitral valve respectively amongthe reference point pixels 311 to 313.

In FIG. 4, a first distance D1 may be provided between the referencepoint pixel 311 and the reference point pixel 312, a second distance D2may be provided between the reference point pixel 311 and the referencepoint pixel 313, and a third distance D3 may be provided between thereference point pixel 312 and the reference point pixel 313.

In the scenario shown in FIG. 4, in response to determining that thefirst distance D1 and the second distance D2 are both greater than thethird distance D3, the processor may respectively define the referencepoint pixels 311 to 313 as the first, second, and third reference pointpixels.

In another embodiment, in response to determining that the seconddistance D2 and the third distance D3 are both greater than the firstdistance D1, the processor 104 may respectively define the referencepoint pixel 313, the reference point pixel 311, and the reference pointpixel 312 as the first, second, and third reference point pixels.Further, in still another embodiment, in response to determining thatthe first distance D1 and the third distance D3 are both greater thanthe second distance D2, the processor 103 may respectively define thereference point pixel 312, the reference point pixel 311, and thereference point pixel 313 as the first, second, and third referencepoint pixels.

In addition, if the heart ultrasound image 30 is determined to be theapical view, among the 3 found reference point pixels, the one with ahighest height should correspond to the apex. Therefore, in FIG. 4, theprocessor 104 may directly define the reference point pixel 311 with thehighest height as the first reference point pixel corresponding to theapex and respectively define the remaining reference point pixels 312and 313 as the second and third reference point pixels corresponding tothe mitral valve, which should however not be construed as limitationsto the disclosure.

Next, the processor 104 may apply the Simpson's rule to estimate theleft ventricular volume corresponding to the heart ultrasound image 30based on the first, second, and third reference point pixels, anddescription details thereof may be found with reference to documents ofthe related art and are not repeated herein.

Besides, in order to improve efficiency of finding the reference pointpixels 311 to 313, the processor 104 may further find the referencepoint pixels 311 to 313 based on mechanisms shown in FIGS. 5A to 5C.

With reference to FIG. 5A, which is a schematic view illustratingfinding of a reference point pixel corresponding to an apex according toFIG. 3. As described above, if the heart ultrasound image 30 isdetermined to be the apical view, among the 3 found reference pointpixels, the one with the highest height should correspond to the apex.

Accordingly, starting from a highest pixel row in the left ventricularmask image 31, the processor 104 may scan down row by row to find thepixel satisfying the above conditions (e.g., the pixel having 8surrounding pixels including 3 first surrounding pixels with the firstvalue and 5 second surrounding pixels with the second value and havingthe first value). In FIG. 5A, when one pixel satisfying the aboveconditions is found, the processor 104 may directly define this pixel asthe reference point pixel 311 corresponding to the apex and stopscanning.

With reference to FIG. 5B, which is a schematic view illustratingfinding of a reference point pixel corresponding to a left flap of amitral valve according to FIG. 5A. In the case that the heart ultrasoundimage 30 is determined to be the apical view, among the 3 foundreference point pixels, the lower left one should correspond to the leftflap of the mitral valve.

Accordingly, starting from a lowest pixel row in the left ventricularmask image 31, the processor 104 may scan from left to right row by rowto find the pixel satisfying the above conditions (e.g., the pixelhaving 8 surrounding pixels including 3 first surrounding pixels withthe first value and 5 second surrounding pixels with the second valueand having the first value). In FIG. 5B, when one pixel satisfying theabove conditions is found, the processor 104 may directly define thispixel as the reference point pixel 312 corresponding to the left flap ofthe mitral valve and stop the scanning process.

With reference to FIG. 5C, which is a schematic view illustratingfinding of a reference point pixel corresponding to a right flap of themitral valve according to FIG. 5B. In the case that the heart ultrasoundimage 30 is determined to be the apical view, among the 3 foundreference point pixels, the lower right one should correspond to theright flap of the mitral valve.

Accordingly, starting from the lowest pixel row in the left ventricularmask image 31, the processor 104 may scan from right to left row by rowto find the pixel satisfying the above conditions (e.g., the pixelhaving 8 surrounding pixels including 3 first surrounding pixels withthe first value and 5 second surrounding pixels with the second valueand having the first value).

In FIG. 5A, when one pixel satisfying the above conditions is found, theprocessor 104 may determine whether this pixel has been defined asanother reference point pixel (e.g., the reference point pixel 312)first. If no is determined, the processor 104 may directly define thispixel as the reference point pixel 313 corresponding to the right flapof the mitral valve and stop the scanning process. In contrast, if thispixel has been defined as another reference point pixel (e.g., thereference point pixel 312), the processor 104 may ignore this pixelfirst and continues to scan upwards to find another pixel satisfying theabove conditions. When another pixel satisfying the above conditions isfound, the processor 104 may directly define the another pixel as thereference point pixel 313 corresponding to the right flap of the mitralvalve and stop the scanning process.

In other embodiments, other manners, in addition to the manners shown inFIG. 5A to

FIG. 5C, may also be adopted by the processor 104 to find the referencepoint pixels 311 to 313 in the left ventricular mask image 31.

Based on the above, it can be seen that in the method for estimating theventricular volume provided by the disclosure, after the leftventricular mask image corresponding to left ventricular ultrasound isobtained, 3 pixels satisfying specific conditions (e.g., the pixelhaving 8 surrounding pixels including 3 first surrounding pixels withthe first value and 5 second surrounding pixels with the second valueand having the first value) are found to act as the reference pointpixels corresponding to the apex and the two flaps of the mitral valve.Thereafter, the left ventricular volume may be estimated based on thereference point pixels. In this way, in the disclosure, the leftventricular volume may be effectively and automatically estimatedwithout the need to manually mark the apex and the flaps on both sidesof the mitral valve.

In other embodiments, the disclosure provides a method for evaluating amovement state of a heart capable of determining whether an abnormalmovement state of the heart occurs based on a change in the leftventricular volume. In the embodiments of the disclosure, the processor104 may access the modules and program codes in the storage circuit 102to implement the method for evaluating the movement state of the heartprovided by the disclosure, and detailed description is provided asfollows.

With reference to FIG. 6, which is a flow chart illustrating a methodfor evaluating a movement state of a heart according to an embodiment ofthe disclosure. The method provided by this embodiment may be executedby the electronic apparatus 100 in FIG. 1, and each step in FIG. 6 isdescribed in detail together with the devices shown in FIG. 1.

First, in step S610, the processor 104 may obtain a plurality ofconsecutive heart ultrasound images corresponding to a heart (e.g., aheart of a specific patient) and accordingly estimate a plurality ofleft ventricular volumes corresponding to the heart ultrasound images.

In an embodiment, the processor 104 may obtain the heart ultrasoundimages first and determine whether each of the heart ultrasound imagesbelongs to the apical view (e.g., A2C or A4C). In an embodiment, theprocessor 104 may determine whether each of the heart ultrasound imagesbelongs to the apical view based on, for example, the technique recordedin the document “Guidelines for Performing a Comprehensive TransthoracicEchocardiographic Examination in Adults: Recommendations from theAmerican Society of Echocardiography”. Since related details may befound with reference to the above document, description thereof is notrepeated herein.

In response to determining that each of the heart ultrasound imagesbelongs to the apical view, the processor 104 may retrieve a leftventricular mask image corresponding to a left ventricle of the heartfrom each of the heart ultrasound images and accordingly estimate a leftventricular volume corresponding to each of the heart ultrasound images.

In an embodiment, the processor 104 may, for example, input each of theheart ultrasound images into the above-mentioned machine learning model,and the machine learning model may output the corresponding leftventricular mask image corresponding to each of the heart ultrasoundimages.

In the embodiments of the disclosure, regarding each of the leftventricular mask images, the processor 104 may estimate thecorresponding left ventricular volume based on the mechanisms taught inFIG. 2 to FIG. 5C, so detailed description is not repeated herein.

In order to facilitate the description of the concept of the disclosure,the following may be supplemented with FIG. 7A for further description,and FIG. 7A is a view illustrating an application scenario according toan embodiment of the disclosure. In FIG. 7A, a plurality of consecutiveleft ventricular volumes obtained in step S610 may be depicted as a leftventricular volume change graph 700 shown in FIG. 7A.

Next, in step S620, the processor 104 may find a plurality of specificextremums 711 to 715 in the left ventricular volumes and accordinglyestimate a plurality of time differences T1 to T4 among the specificextremums 711 to 715.

In an embodiment, the processor 104 may treat, but not limited to, aplurality of specific left ventricular volumes corresponding toend-diastolic volumes (EDVs) s among the left ventricular volumes as thespecific extremums, for example. By definition, each EDV shouldcorrespond to the largest left ventricular volume in a heart rate cycleto which it belongs. Based on this, if the processor 104 determines thatan i^(th) (i is an integer) left ventricular volume among the leftventricular volumes is greater than an i-^(th) left ventricular volumeand an i+i^(th) left ventricular volume, the processor 104 may thendetermine that the i^(th) left ventricular volume should correspond tothe EDV and may further determine that the i^(th) left ventricularvolume belongs to one of the specific extremums.

In the scenario in FIG. 7A, since the left ventricular volume changegraph 700 may be understood has including 5 heart rate cycles, theprocessor 104 may find 5 EDVs as the specific extremums 711 to 715according to the above principle. Next, the processor 104 may thenestimate the time differences T1 to T4 among the specific extremums 711to 715.

Roughly speaking, it is assumed that the specific extremums found by theprocessor 104 include a 1^(st) specific extremum to a K^(th) (where K isan integer) specific extremum, and a time difference between a i^(th)specific extremum and a i^(th) specific extremum may be defined as ai^(th) time difference, where 1≤j≤K−1.

Taking FIG. 7A as an example, the time difference T1 (may be understoodas a 1^(st) time difference) is, for example, the time differencebetween the specific extremum 711 (may be understood as the 1^(st)specific extremum) and the specific extremum 712 (may be understood as a2^(nd) specific extremum). The time difference T2 (may be understood asa 2^(nd) time difference) is, for example, the time difference betweenthe specific extremum 712 (may be understood as the 2^(nd) specificextremum) and the specific extremum 713 (may be understood as a 3^(rd)specific extremum). The time difference T3 (may be understood as a 3-ndtime difference) is, for example, the time difference between thespecific extremum 713 (may be understood as the 3^(rd) specificextremum) and the specific extremum 714 (may be understood as a 4^(th)specific extremum). The time difference T4 (may be understood as a4^(th) time difference) is, for example, the time difference between thespecific extremum 714 (may be understood as the 4^(th) specificextremum) and the specific extremum 715 (may be understood as a 5^(th)specific extremum), which should however not be construed as limitationsto the disclosure.

After that, in step S630, the processor 104 may estimate a statisticalcharacteristic value (including but not limited to an average value ofthe time differences T1 to T4) of the time differences T1 to T4 based onthe time differences T1 to T4. Further, the processor 104 may determinewhether each of the time differences T1 to T4 deviates from thestatistical characteristic value up to a predetermined threshold. Indifferent embodiments, the predetermined threshold may be set to anyratio value according to a designer's needs, such as, but not limitedto, 5%.

In step S640, in response to determining that at least one of the timedifferences T1 to T4 deviates from the statistical characteristic valueup to the predetermined threshold, the processor 104 may determine thatthe abnormal movement state (e.g., an arrhythmia state) of the heartoccurs. In contrast, in response to determining that all of the timedifferences T1 to T4 do not deviate from the statistical characteristicvalue up to the predetermined threshold, the processor 104 may determinethat the abnormal movement state of the heart does not occur.

In FIG. 7A, it is assumed that if the processor 104 determines that allof the time differences T1 to T4 do not deviate from the statisticalcharacteristic value up to the predetermined threshold, the processor104 may then determine that the abnormal movement state of the heart,such as the arrhythmia state and the like, does not occur.

With reference to FIG. 7B, which is a view illustrating anotherapplication scenario according to FIG. 7A. In this embodiment, it isassumed that the processor 104 obtains a left ventricular volume changegraph 700 a as shown in FIG. 7B according to the above teachings andfinds a plurality of specific extremums 711 a to 715 a corresponding tothe EDVs and corresponding time differences T1′ to T4′.

In FIG. 7B, it is assumed that the processor 104 determines that thetime difference T2′ among the time differences T1′ to T4′ deviates froma statistical characteristic value of the time differences T1′ to T4′ upto a predetermined threshold, the processor 104 may then determine thatthe abnormal movement state (e.g., the arrhythmia state) of the heartoccurs, which should however not be construed as limitations to thedisclosure.

In the embodiments of the disclosure, the processor 104 may provide therelevant medical personnel with, but not limited to, the determinationresult of whether the abnormal movement state of the heart occurs, as areference for diagnosis.

In addition, although the left ventricular volumes corresponding to theEDVs are used as the considered specific extreme values in the aboveembodiments, in other embodiments, the processor 104 may also use theleft ventricular volumes corresponding to end-systolic volumes (ESVs) asthe considered specific extreme values. By definition, each ESV shouldcorrespond to the smallest left ventricular volume in the heart ratecycle to which it belongs. Based on this, if the processor 104determines that the i^(th) left ventricular volume among the leftventricular volumes is less than the i-1^(th) left ventricular volumeand the i+l^(th) left ventricular volume, the processor 104 may thendetermine that the i^(th) left ventricular volume should correspond tothe ESV and may further determine that the i^(th) left ventricularvolume belongs to one of the specific extremums.

Based on this, in the scenario shown in FIG. 7B, the processor 104 maycorrespondingly find the left ventricular volumes corresponding to theESVs to act as specific extremums 711 b to 715 b and accordinglyestimate corresponding time differences T1″ to T4″.

In FIG. 7B, it is assumed that the processor 104 determines that thetime difference T1″ among the time differences T1″ to T4″ deviates froma statistical characteristic value of the time differences T1″ to T4″ upto a predetermined threshold, the processor 104 may then determine thatthe abnormal movement state (e.g., the arrhythmia state) of the heartoccurs, which may be used as a reference for diagnosis for relatedmedical personnel.

In some embodiments, if the relevant medical personnel determines thatthe abnormal movement state of the heart is misjudged as occurring afterexamining the heart ultrasound image corresponding to FIG. 7B (that is,the abnormal movement state of the heart does not actually occurs), therelevant medical personnel may report this situation to the electronicapparatus 100.

In the embodiments of the disclosure, since the above misjudgment may becaused by an unfavorable recognition ability of the machine learningmodel for the left ventricular image region, the processor 104 mayretrain the machine learning model accordingly to reduce the probabilityof misjudgment in the future, which should however not be construed aslimitations to the disclosure.

In view of the foregoing, in the method for estimating the ventricularvolume provided by the disclosure, after the left ventricular mask imagecorresponding to left ventricular ultrasound is obtained, 3 pixelssatisfying the specific conditions are found to act as the referencepoint pixels corresponding to the apex and the two flaps of the mitralvalve. Thereafter, the left ventricular volume may be estimated based onthe reference point pixels. In this way, in the disclosure, the leftventricular volume may be effectively and automatically estimatedwithout the need to manually mark the apex and the flaps on both sidesof the mitral valve.

In addition, in the method for evaluating the movement state of theheart provided by the disclosure, the specific extremums correspondingto the EDVs (or ESVs) may be found in the left ventricular volumes ofthe consecutive heart ultrasound images, and whether the abnormalmovement state of the heart such as arrhythmia occurs may be determinedbased on the time differences among the specific extremums. In this way,relevant medical personnel may easily learn the condition of the heart,and the probability of making incorrect assessments (for example,incorrect ejection fraction is calculated, etc.) is therefore lowered.

It will be apparent to those skilled in the art that variousmodifications and variations can be made to the disclosed embodimentswithout departing from the scope or spirit of the disclosure. In view ofthe foregoing, it is intended that the disclosure covers modificationsand variations provided that they fall within the scope of the followingclaims and their equivalents.

What is claimed is:
 1. A method for evaluating a movement state of aheart, suitable for an electronic apparatus, the method comprising:obtaining a plurality of consecutive heart ultrasound imagescorresponding to a heart and accordingly estimating a plurality of leftventricular volumes corresponding to the heart ultrasound images;finding a plurality of specific extremums in the left ventricularvolumes and accordingly estimating a plurality of time differences amongthe specific extremums; estimating a statistical characteristic value ofthe time differences based on the time differences; and determining thatan abnormal movement state of the heart occurs in response todetermining that at least one of the time differences deviates from thestatistical characteristic value up to a predetermined threshold.
 2. Themethod according to claim 1, wherein the step of obtaining theconsecutive heart ultrasound images corresponding to the heart andaccordingly estimating the left ventricular volumes corresponding to theheart ultrasound images comprises: obtaining the heart ultrasound imagesand determine whether each of the heart ultrasound images belongs to anapical view; and retrieving a left ventricular mask image correspondingto a left ventricle of the heart from each of the heart ultrasoundimages and accordingly estimating a left ventricular volumecorresponding to each of the heart ultrasound images in response todetermining that each of the heart ultrasound images belongs to theapical view.
 3. The method according to claim 2, wherein the step ofretrieving the left ventricular mask image corresponding to the leftventricle of the heart from each of the heart ultrasound imagescomprises: inputting each of the heart ultrasound images into a machinelearning model, wherein the machine learning model outputs thecorresponding left ventricular mask image in response to each of theheart ultrasound images.
 4. The method according to claim 3, furthercomprising: retraining the machine learning model in response todetermining that the abnormal movement state of the heart is misjudgedas occurring.
 5. The method according to claim 1, wherein the step offinding the specific extremums in the left ventricular volumescomprises: determining that an i^(th) left ventricular volume belongs toone of the specific extremums in response to determining that the i^(th)left ventricular volume among the left ventricular volumes is greaterthan an i-1^(th) left ventricular volume and an i+1^(th) leftventricular volume, wherein i is an integer.
 6. The method according toclaim 1, wherein the step of finding the specific extremums in the leftventricular volumes comprises: determining that an i^(th) leftventricular volume belongs to one of the specific extremums in responseto determining that the i^(th) left ventricular volume among the leftventricular volumes is less than an i-1^(th) left ventricular volume andan i+1^(th) left ventricular volume.
 7. The method according to claim 1,wherein the specific extremums comprise a 1^(st) specific extremum to aK^(th) specific extremum, and the time differences comprise a 1^(st)time difference to a K−1^(th) time difference, wherein a i^(th) timedifference among the time differences is a time difference between aj+1^(th) specific extremum and a j^(th) specific extremum, and 1≤j≤K−1,wherein K is an integer.
 8. The method according to claim 1, wherein thestatistical characteristic value is an average value of the timedifferences.
 9. The method according to claim 1, wherein the abnormalmovement state comprises an arrhythmia state.